Algorithms

This section contains links to information, examples, use cases, etc. for the various algorithms we intend to implement. Click the individual links to learn more. The initial algorithms descriptions have been copied here from the original project proposal. The algorithms are grouped by the application setting, they can be used for. In case of multiple applications, the version presented in the paper was chosen, versions as implemented in our project will be added as soon as we are working on them.

Original Paper: Map Reduce for Machine Learning on Multicore

Papers related to Map Reduce:

For Papers, videos and books related to machine learning in general, see Machine Learning Resources

All algorithms are either marked as integrated, that is the implementation is integrated into the development version of Mahout. Algorithms that are currently being developed are annotated with a link to the JIRA issue that deals with the specific implementation. Usually these issues already contain patches that are more or less major, depending on how much work was spent on the issue so far. Algorithms that have so far not been touched are marked as open.

What, When, Where, Why (but not How or Who) - Community tips, tricks, etc. for when to use which algorithm in what situations, what to watch out for in terms of errors. That is, practical advice on using Mahout for your problems.

Classification

A general introduction to the most common text classification algorithms can be found at Google Answers: http://answers.google.com/answers/main?cmd=threadview&id=225316 For information on the algorithms implemented in Mahout (or scheduled for implementation) please visit the following pages.

Logistic Regression (SGD implementation in progress: MAHOUT-228)

Bayesian

Support Vector Machines (SVM) (open: MAHOUT-14 and MAHOUT-232)

Perceptron and Winnow (open: MAHOUT-85)

Neural Network (open, but MAHOUT-228 might help)

Random Forests (integrated - MAHOUT-122, MAHOUT-140, MAHOUT-145)

Clustering

Reference Reading

Canopy Clustering (integrated)

k-Means (integrated)

Fuzzy K-Means (MAHOUT-74) (integrated)

Expectation Maximization (EM) (MAHOUT-28)

Mean Shift (integrated)

Hierarchical Clustering (MAHOUT-19)

Dirichlet Process Clustering (MAHOUT-30 - integrated)
Latent Dirichlet Allocation (MAHOUT-123 - integrated)

Regression

Locally Weighted Linear Regression (open)

Dimension reduction

Principal Components Analysis (PCA) (open)

Independent Component Analysis (open)

Gaussian Discriminative Analysis (GDA) (open)

Evolutionary Algorithms

Non map reduce algorithms

Some algorithms and applications appeared on the mailing list, that have not been published in map reduce form so far. As we do not restrict ourselves to hadoop-only versions, these proposals are listed here.

Hidden Markov Models (HMM) (open)

Recommendation Learning (integrated)